Anastasiia Koloskova

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension ...

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches are limited by network bandwidth, we propose t ...

Decentralized optimization methods enable on-device training of machine learning models without a central coordinator. In many scenarios communication between devices is energy demanding and time consuming and forms the bottleneck of the entire system. We ...

We consider decentralized stochastic optimization with the objective function (e.g. data samples for machine learning task) being distributed over n machines that can only communicate to their neighbors on a fixed communication graph. To reduce the communi ...

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly because of their cheap per iteration cost, data locality, and their communication-efficiency. In this paper we introduce a unified convergence analysis that cover ...

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

Anastasiia Koloskova

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Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Efficient Greedy Coordinate Descent for Composite Problems

Decentralized deep learning with arbitrary communication compression

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

The Privacy Power of Correlated Noise in Decentralized Learning